[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-event-driven-data-pipelines-watchdog-pandas-summary":3,"summaries-facets-categories":74,"summary-related-event-driven-data-pipelines-watchdog-pandas-summary":3659},{"id":4,"title":5,"ai":6,"body":13,"categories":51,"created_at":53,"date_modified":53,"description":45,"extension":54,"faq":53,"featured":55,"kicker_label":53,"meta":56,"navigation":57,"path":58,"published_at":59,"question":53,"scraped_at":53,"seo":60,"sitemap":61,"source_id":62,"source_name":63,"source_type":64,"source_url":65,"stem":66,"tags":67,"thumbnail_url":53,"tldr":71,"tweet":53,"unknown_tags":72,"__hash__":73},"summaries\u002Fsummaries\u002Fevent-driven-data-pipelines-watchdog-pandas-summary.md","Event-Driven Data Pipelines: Watchdog + Pandas",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",3672,1993,14921,0.00170825,{"type":14,"value":15,"toc":44},"minimark",[16,21,30,34,37,41],[17,18,20],"h2",{"id":19},"pollings-hidden-costs-and-event-driven-fix","Polling's Hidden Costs and Event-Driven Fix",[22,23,24,25,29],"p",{},"Manual scripts force explicit runs for new files in a folder, while polling via CRON or ",[26,27,28],"code",{},"while True"," loops checks repeatedly—wasting CPU cycles on empty folders and delaying processing until the next interval. Event-driven listening with Watchdog solves this by reacting only to actual filesystem events like file creation, enabling near-instant data ingestion without idle overhead.",[17,31,33],{"id":32},"building-the-reactive-pipeline","Building the Reactive Pipeline",[22,35,36],{},"Monitor a target directory for incoming files using Watchdog's observer pattern, then pipe events directly to Pandas for cleaning and processing. The article outlines a step-by-step implementation: set up the event handler, define processing logic in Pandas (e.g., load CSV, transform data), and run the observer daemonized for always-on operation.",[17,38,40],{"id":39},"production-trade-offs","Production Trade-offs",[22,42,43],{},"For reliability, handle edge cases like duplicate events or partial writes by adding file locks or size checks before processing. 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",[26,3678,3679],{},"implied_quote_history"," is append-only, preserving every snapshot with ",[26,3682,3683],{},"id"," autoincrement primary key, ",[26,3686,3687],{},"asof_ts"," (UTC ISO timestamp per poll), and ",[26,3690,3691],{},"option_key"," (stable identifier: ",[26,3694,3695],{},"symbol|expiry|strike|cp|at|ts",") as join key. Indexes on ",[26,3698,3699],{},"(symbol, expiry, asof_ts)"," and ",[26,3702,3703],{},"(option_key, asof_ts)"," speed expiry-time or option-timeline queries. Columns capture surface IV (",[26,3706,3707],{},"s_vol","), ATM vol (",[26,3710,3711],{},"atm_vol","), Greeks (delta, gamma, theta, vega), underlying price (",[26,3714,3715],{},"u_prc","), years to expiry (",[26,3718,3719],{},"years","), rate, bid\u002Fask\u002FIVs, ",[26,3722,3723],{},"calc_source"," (filter to \"Loop\" for consistent snapshots), ",[26,3726,3727],{},"quote_ok"," flag (1 if bid\u002Fask non-zero), and ",[26,3730,3731],{},"src_ts",".",[22,3734,3735,3738,3739,3741,3742,3745,3746,3749,3750,3700,3753,3756],{},[26,3736,3737],{},"implied_quote_latest"," uses ",[26,3740,3691],{}," primary key for upserts: each poll overwrites with newest values, setting ",[26,3743,3744],{},"last_asof_ts"," to current snapshot time. Same columns and index on ",[26,3747,3748],{},"(symbol, expiry)",". PRAGMA ",[26,3751,3752],{},"journal_mode=WAL",[26,3754,3755],{},"synchronous=NORMAL"," ensure reliable writes. This split avoids full-history scans for \"current surface\" while retaining audit trail—history grows unbounded (e.g., 1454 rows\u002Fsnapshot × 9 polls = 12,806 total), latest stays flat at ~1454 rows.",[17,3758,3760],{"id":3759},"normalize-and-poll-api-for-reliable-snapshots","Normalize and Poll API for Reliable Snapshots",[22,3762,3763,3764,3767,3768,3771,3772,3775,3776,3775,3779,3775,3782,3785,3786,3789,3790,3775,3793,3796,3797,3732],{},"Fetch via REST ",[26,3765,3766],{},"getmsgs"," on ",[26,3769,3770],{},"https:\u002F\u002Fmlink-live.nms.saturn.spiderrockconnect.com\u002Frest\u002Fjson"," with ",[26,3773,3774],{},"apiKey",", ",[26,3777,3778],{},"msgType=LiveImpliedQuote",[26,3780,3781],{},"where=okey.tk:eq:TSLA",[26,3783,3784],{},"limit=2000",". Response: list of messages ending in ",[26,3787,3788],{},"QueryResult","; filter to ",[26,3791,3792],{},"mTyp=LiveImpliedQuote",[26,3794,3795],{},"calcSource=Loop",", non-zero ",[26,3798,3799],{},"sVol",[22,3801,3802,3803,3806,3807,3809,3810,3813,3814,3816,3817,3819,3820,3823],{},"Flatten nested ",[26,3804,3805],{},"pkey.okey"," into ",[26,3808,3691],{}," via ",[26,3811,3812],{},"|",". Build DataFrame rows with all fields; sort by ",[26,3815,3731],{},", dedupe latest per ",[26,3818,3691],{},". ",[26,3821,3822],{},"quote_ok = int(not (o_bid == 0 and o_ask == 0))"," flags quoted options without dropping analytics-only rows.",[22,3825,3826,3827,3830,3831,3834,3835,3837,3838,3841,3842,3845],{},"Loop polls every ",[26,3828,3829],{},"poll_interval_s=10"," for ",[26,3832,3833],{},"poll_duration_s=120",": timestamp ",[26,3836,3687],{},", fetch\u002Fnormalize\u002Fwrite. Batch ",[26,3839,3840],{},"executemany"," inserts history; upsert latest with ",[26,3843,3844],{},"on conflict(option_key) do update set"," all fields. Handles varying row counts (e.g., 1454 → snapshot_rows fluctuates due to limit). Production tip: pin expiries\u002Fstrikes or interpolate to fixed moneyness for stability.",[17,3847,3849],{"id":3848},"reconstruct-smiles-skew-and-metrics-from-history-queries","Reconstruct Smiles, Skew, and Metrics from History Queries",[22,3851,3852,3853,3856,3857,3860,3861,3864,3865,3867,3868,3871],{},"Query history for analysis: count rows per expiry (",[26,3854,3855],{},"group by expiry order by n desc limit 10",") to pick representative like ",[26,3858,3859],{},"2026-11-20"," (highest coverage). Pull ",[26,3862,3863],{},"asof_ts, strike, cp, s_vol, u_prc"," for expiry\u002Fsymbol; filter calls; plot ",[26,3866,3707],{}," vs strike for timestamps (first\u002Fmid\u002Flast of ",[26,3869,3870],{},"ts_list",").",[22,3873,3874,3875,3878,3879,3882,3883,3886,3887,3732],{},"Zoom near spot: ",[26,3876,3877],{},"s0 = u_prc.median()",", strikes in ",[26,3880,3881],{},"[s0*0.6, s0*1.4]"," reveals ATM shifts invisible in full range. Enables questions like \"TSLA surface at 10:32?\" or \"when skew steepened?\"—replay via ",[26,3884,3885],{},"where symbol=? and expiry=?"," or ",[26,3888,3889],{},"option_key, asof_ts",[22,3891,3892,3893,3895,3896,3899],{},"Track evolution: query timelines per option\u002Fexpiry to compute ATM IV (min ",[26,3894,3707],{}," near spot), skew proxies (wing vs ATM deltas). Stored ",[26,3897,3898],{},"u_prc, years, rate"," support smile rebuilds or Greeks audits without re-API calls. Trade-off: API fees for data; limit caps chains; no interpolation here keeps ingestion simple but may vary strikes across polls.",{"title":45,"searchDepth":46,"depth":46,"links":3901},[3902,3903,3904],{"id":3672,"depth":46,"text":3673},{"id":3759,"depth":46,"text":3760},{"id":3848,"depth":46,"text":3849},[128],{"content_references":3907,"triage":3913},[3908],{"type":3909,"title":3910,"url":3911,"context":3912},"tool","SpiderRock MLink LiveImpliedQuote","https:\u002F\u002Fdocs.spiderrockconnect.com\u002Fdocs\u002Fnext\u002FMessageSchemas\u002FSchema\u002FTopics\u002Fanalytics\u002FLiveImpliedQuote\u002F","mentioned",{"relevance":3914,"novelty":3915,"quality":3914,"actionability":3914,"composite":3916,"reasoning":3917},4,3,3.8,"Category: AI Automation. The article provides a practical guide on building a queryable database from live API data, addressing the audience's need for actionable content in automation. It details a specific implementation using SQLite and Python, which can be directly applied by developers looking to integrate live data into their products.","\u002Fsummaries\u002F9083ba0dfd966742-build-queryable-options-iv-db-from-live-api-polls-summary","2026-05-03 16:03:23","2026-05-03 17:01:13",{"title":3662,"description":45},{"loc":3918},"9083ba0dfd966742","Data Driven Investor","https:\u002F\u002Fmedium.datadriveninvestor.com\u002Ffrom-live-options-analytics-to-a-queryable-database-in-python-95fd1bd4ea92?source=rss----32881626c9c9---4","summaries\u002F9083ba0dfd966742-build-queryable-options-iv-db-from-live-api-polls-summary",[68,70,69],"Capture SpiderRock LiveImpliedQuote snapshots for TSLA every 10s into SQLite: append full history for audits (12k+ rows in 2min), upsert latest view per option_key. Query to reconstruct vol smiles and track ATM IV\u002Fskew changes over time.",[],"mCfcLLxXWQrSwDkhpH5mS0KYJ0Zntifa3yboHqyovqg",{"id":3932,"title":3933,"ai":3934,"body":3939,"categories":3989,"created_at":53,"date_modified":53,"description":45,"extension":54,"faq":53,"featured":55,"kicker_label":53,"meta":3990,"navigation":57,"path":4008,"published_at":4009,"question":53,"scraped_at":4010,"seo":4011,"sitemap":4012,"source_id":4013,"source_name":4014,"source_type":64,"source_url":4015,"stem":4016,"tags":4017,"thumbnail_url":53,"tldr":4019,"tweet":53,"unknown_tags":4020,"__hash__":4021},"summaries\u002Fsummaries\u002F56100a2f235e4ed4-production-ml-pipelines-with-zenml-custom-material-summary.md","Production ML Pipelines with ZenML: Custom Materializers & HPO",{"provider":7,"model":8,"input_tokens":3935,"output_tokens":3936,"processing_time_ms":3937,"cost_usd":3938},9247,2138,40785,0.0028959,{"type":14,"value":3940,"toc":3983},[3941,3945,3948,3952,3960,3964,3976,3980],[17,3942,3944],{"id":3943},"custom-materializers-enable-metadata-rich-data-handling","Custom Materializers Enable Metadata-Rich Data Handling",[22,3946,3947],{},"Define DatasetBundle to encapsulate X, y, feature_names, and stats from sklearn's load_breast_cancer (569 samples, 30 features). Pair it with DatasetBundleMaterializer inheriting BaseMaterializer: save() stores X.npy, y.npy, and meta.json with feature_names\u002Fstats; load() reconstructs from files; extract_metadata() computes n_samples, n_features, class_distribution (e.g., {0: 357, 1: 212}). This auto-logs queryable metadata to artifacts, ensuring domain objects serialize seamlessly without pickling issues, while supporting ZenML's reproducibility.",[17,3949,3951],{"id":3950},"modular-steps-log-hyperparameters-and-metrics-at-every-stage","Modular Steps Log Hyperparameters and Metrics at Every Stage",[22,3953,3954,3955,3959],{},"Use @step(enable_cache=True) for load_data() returning Annotated",[3956,3957,3958],"span",{},"DatasetBundle, \"raw_dataset\"",". split_and_scale() performs stratified train_test_split (default test_size=0.2), StandardScaler fit\u002Ftransform, logs train_size\u002Ftest_size via log_metadata(). train_candidate() supports model_type=\"random_forest\"|\"gradient_boosting\"|\"logistic\" with n_estimators=100, max_depth=5 defaults, fits on X_train\u002Fy_train, logs model_type\u002Fhyperparameters. evaluate_candidate() computes accuracy, f1, roc_auc on X_test\u002Fy_test (using predict_proba if available), logs all metrics with label. These steps cache outputs, track lineage, and expose metadata for debugging\u002Fproduction monitoring.",[17,3961,3963],{"id":3962},"fan-out-hpo-and-fan-in-selection-promote-best-model","Fan-Out HPO and Fan-In Selection Promote Best Model",[22,3965,3966,3967,3971,3972,3975],{},"SEARCH_SPACE defines 4 configs: {\"model_type\": \"random_forest\", \"n_estimators\": 50\u002F200, \"max_depth\": 3\u002F7}, {\"gradient_boosting\": 100\u002F3}, {\"logistic\":1\u002F1}. @pipeline(model=PRODUCTION_MODEL) training_pipeline() fans out: load_data → split_and_scale → loop over train_candidate(id=f\"train_",[3968,3969,3970],"em",{"i":45},"\") and evaluate_candidate(id=f\"eval","\", label=f\"{type}(n={n},d={d})\"). Fan-in via select_best(): picks max ROC AUC index, logs winning_metrics\u002Fchosen_candidate to model metadata, returns production_model to versioned breast_cancer_classifier (tags=",[3956,3973,3974],{},"\"tutorial\",\"advanced\"","). Generates 8 step runs (4 train+4 eval), automates promotion via Model control plane.",[17,3977,3979],{"id":3978},"client-api-ensures-inspection-caching-and-zero-recompute-reruns","Client API Ensures Inspection, Caching, and Zero-Recompute Reruns",[22,3981,3982],{},"Post-run, Client().get_pipeline_run() shows status, step counts (e.g., 9 steps), aggregated metadata. get_model_version(\"latest\") reveals version.number, linked artifacts, run_metadata (e.g., chosen_candidate). Reload prod_model = get_artifact_version(\"production_model\").load(), verify accuracy_score on stored X_test\u002Fy_test. raw_dataset metadata includes n_samples=569, n_features=30, class_distribution. Rerun hits cache (enable_cache=True), skips recompute. list_pipeline_runs(), list_model_versions(), list_artifact_versions() enable querying; full notebook at GitHub confirms 100% reproducibility without redundant work.",{"title":45,"searchDepth":46,"depth":46,"links":3984},[3985,3986,3987,3988],{"id":3943,"depth":46,"text":3944},{"id":3950,"depth":46,"text":3951},{"id":3962,"depth":46,"text":3963},{"id":3978,"depth":46,"text":3979},[128],{"content_references":3991,"triage":4004},[3992,3995,4000],{"type":3909,"title":3993,"url":3994,"context":3912},"ZenML","https:\u002F\u002Fgithub.com\u002Fzenml-io\u002Fzenml",{"type":3996,"title":3997,"url":3998,"context":3999},"other","zenml_advanced_end_to_end_pipeline_Marktechpost.ipynb","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fzenml_advanced_end_to_end_pipeline_Marktechpost.ipynb","recommended",{"type":4001,"title":4002,"author":4003,"context":3912},"dataset","breast_cancer","sklearn.datasets",{"relevance":4005,"novelty":3914,"quality":3914,"actionability":4005,"composite":4006,"reasoning":4007},5,4.55,"Category: AI Automation. The article provides a detailed guide on building production-grade ML pipelines using ZenML, addressing practical aspects like custom materializers and hyperparameter optimization, which are crucial for the target audience. It includes specific steps and code examples that the audience can directly implement in their projects.","\u002Fsummaries\u002F56100a2f235e4ed4-production-ml-pipelines-with-zenml-custom-material-summary","2026-05-04 22:11:37","2026-05-05 16:09:56",{"title":3933,"description":45},{"loc":4008},"56100a2f235e4ed4","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F04\u002Fhow-to-build-an-end-to-end-production-grade-machine-learning-pipeline-with-zenml-including-custom-materializers-metadata-tracking-and-hyperparameter-optimization\u002F","summaries\u002F56100a2f235e4ed4-production-ml-pipelines-with-zenml-custom-material-summary",[4018,68,70,69],"machine-learning","ZenML enables end-to-end ML pipelines with custom DatasetBundle materializers for metadata-rich serialization, fan-out over 4 hyperparameter configs for RandomForest\u002FGradientBoosting\u002FLogisticRegression, fan-in best-model selection by ROC AUC, full artifact tracking, and cache-driven reproducibility on breast cancer dataset.",[],"_jyeZef15FOC-726KyxSOjynaY54SFmoVQfVvb811WU",{"id":4023,"title":4024,"ai":4025,"body":4030,"categories":4283,"created_at":53,"date_modified":53,"description":45,"extension":54,"faq":53,"featured":55,"kicker_label":53,"meta":4284,"navigation":57,"path":4297,"published_at":4298,"question":53,"scraped_at":4299,"seo":4300,"sitemap":4301,"source_id":4302,"source_name":4303,"source_type":64,"source_url":4304,"stem":4305,"tags":4306,"thumbnail_url":53,"tldr":4308,"tweet":53,"unknown_tags":4309,"__hash__":4310},"summaries\u002Fsummaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary.md","Automate Weekly PDF Reports with Python ETL Pipeline",{"provider":7,"model":8,"input_tokens":4026,"output_tokens":4027,"processing_time_ms":4028,"cost_usd":4029},8933,2254,17256,0.00289095,{"type":14,"value":4031,"toc":4278},[4032,4036,4039,4097,4112,4128,4138,4141,4145,4148,4193,4196,4199,4202,4206,4209,4212,4268,4271,4274],[17,4033,4035],{"id":4034},"merge-raw-datasets-into-actionable-business-data","Merge Raw Datasets into Actionable Business Data",[22,4037,4038],{},"Start by loading six Olist e-commerce CSVs (orders, customers, items, payments, products, reviews) with pandas.read_csv, then merge on keys like customer_id, order_id, product_id:",[4040,4041,4044],"pre",{"className":4042,"code":4043,"language":68,"meta":45,"style":45},"language-python shiki shiki-themes github-light github-dark","def load_data():\n    return {\n        \"orders\": pd.read_csv(\"data\u002Folist_orders_dataset.csv\"),\n        # ... other datasets\n    }\n\ndf = data[\"orders\"].merge(data[\"customers\"], on=\"customer_id\", how=\"left\") \\\n    .merge(data[\"items\"], on=\"order_id\", how=\"left\") \\\n    # ... other merges\n",[26,4045,4046,4053,4058,4063,4068,4073,4079,4085,4091],{"__ignoreMap":45},[3956,4047,4050],{"class":4048,"line":4049},"line",1,[3956,4051,4052],{},"def load_data():\n",[3956,4054,4055],{"class":4048,"line":46},[3956,4056,4057],{},"    return {\n",[3956,4059,4060],{"class":4048,"line":3915},[3956,4061,4062],{},"        \"orders\": pd.read_csv(\"data\u002Folist_orders_dataset.csv\"),\n",[3956,4064,4065],{"class":4048,"line":3914},[3956,4066,4067],{},"        # ... other datasets\n",[3956,4069,4070],{"class":4048,"line":4005},[3956,4071,4072],{},"    }\n",[3956,4074,4076],{"class":4048,"line":4075},6,[3956,4077,4078],{"emptyLinePlaceholder":57},"\n",[3956,4080,4082],{"class":4048,"line":4081},7,[3956,4083,4084],{},"df = data[\"orders\"].merge(data[\"customers\"], on=\"customer_id\", how=\"left\") \\\n",[3956,4086,4088],{"class":4048,"line":4087},8,[3956,4089,4090],{},"    .merge(data[\"items\"], on=\"order_id\", how=\"left\") \\\n",[3956,4092,4094],{"class":4048,"line":4093},9,[3956,4095,4096],{},"    # ... other merges\n",[22,4098,4099,4100,4103,4104,4107,4108,4111],{},"Convert timestamps to datetime for time-based calcs: df",[3956,4101,4102],{},"\"order_purchase_timestamp\""," = pd.to_datetime(...). Compute delivery delays as (delivered - estimated).dt.days > 0 for is_delayed. Derive revenue = price + freight_value, profit = price - freight_value. Aggregate metrics like revenue_current = df",[3956,4105,4106],{},"\"revenue\"",".sum(), orders_current = df",[3956,4109,4110],{},"\"order_id\"",".nunique(), AOV = revenue \u002F orders.",[22,4113,4114,4115,4118,4119,4121,4122,4118,4125,4127],{},"Group by month for trends: monthly = df.groupby(\"month\").agg({\"revenue\": \"sum\", \"order_id\": \"nunique\"}); monthly",[3956,4116,4117],{},"\"growth\""," = monthly",[3956,4120,4106],{},".pct_change() * 100; monthly",[3956,4123,4124],{},"\"moving_avg\"",[3956,4126,4106],{},".rolling(3).mean().",[22,4129,4130,4131,4137],{},"Simulate weekly reporting with cutoff: df_sim = df",[3956,4132,4133,4134,4136],{},"df",[3956,4135,4102],{}," \u003C= cutoff_date",", advancing cutoff_date = start_date + pd.Timedelta(days=7 * run_count) via state.txt to mimic live cycles without reprocessing all history.",[22,4139,4140],{},"This standardization ensures consistent metric definitions across runs, turning scattered CSVs into a unified view of who bought what, payment amounts, delivery times, and satisfaction.",[17,4142,4144],{"id":4143},"add-rule-based-insights-and-build-pdf-reports","Add Rule-Based Insights and Build PDF Reports",[22,4146,4147],{},"Metrics alone fail without context—use simple if-conditions to interpret:",[4040,4149,4151],{"className":4042,"code":4150,"language":68,"meta":45,"style":45},"def generate_insights(metrics):\n    insights = []\n    if metrics[\"profit_current\"] \u003C metrics[\"revenue_current\"]:\n        insights.append(\"Revenue growing but profit margin thin, high logistics costs.\")\n    growth_volatility = metrics[\"monthly\"][\"growth\"].std()\n    if growth_volatility > 50:\n        insights.append(\"Revenue growth highly volatile, unstable performance.\")\n    # ...\n",[26,4152,4153,4158,4163,4168,4173,4178,4183,4188],{"__ignoreMap":45},[3956,4154,4155],{"class":4048,"line":4049},[3956,4156,4157],{},"def generate_insights(metrics):\n",[3956,4159,4160],{"class":4048,"line":46},[3956,4161,4162],{},"    insights = []\n",[3956,4164,4165],{"class":4048,"line":3915},[3956,4166,4167],{},"    if metrics[\"profit_current\"] \u003C metrics[\"revenue_current\"]:\n",[3956,4169,4170],{"class":4048,"line":3914},[3956,4171,4172],{},"        insights.append(\"Revenue growing but profit margin thin, high logistics costs.\")\n",[3956,4174,4175],{"class":4048,"line":4005},[3956,4176,4177],{},"    growth_volatility = metrics[\"monthly\"][\"growth\"].std()\n",[3956,4179,4180],{"class":4048,"line":4075},[3956,4181,4182],{},"    if growth_volatility > 50:\n",[3956,4184,4185],{"class":4048,"line":4081},[3956,4186,4187],{},"        insights.append(\"Revenue growth highly volatile, unstable performance.\")\n",[3956,4189,4190],{"class":4048,"line":4087},[3956,4191,4192],{},"    # ...\n",[22,4194,4195],{},"Generate PDF with ReportLab: create executive summary (e.g., 2018 revenue \u003C 2017, orders down, AOV stable, 9.36% delay rate, 3.91 avg review score), KPI trends (Jan 2018 revenue\u002Fprofit >600% over 2017 but slowing; AOV 2-14% lower, driven by transaction volume), top products (relogios_presentes\u002Fbeleza_saude ~510K revenue each), delivery (SE state 33% delays, casa_conforto_2 60%; overall -10.76 avg delay days = early deliveries), payments (credit card 75%, boleto 19.1%), reviews (5-stars dominant, avg 3.91).",[22,4197,4198],{},"Key patterns: thin margins from costs; volatile growth; new-customer reliance; delays hurt scores; SP top region; credit users spend more.",[22,4200,4201],{},"Code charts with matplotlib (plt.savefig(\"revenue_chart.png\")), insert via Image(width=450,height=220), tables via Table(table_data). Central pipeline: data → transform → metrics → insights → generate_report().",[17,4203,4205],{"id":4204},"schedule-email-delivery-with-github-actions","Schedule Email Delivery with GitHub Actions",[22,4207,4208],{},"Automate email: use smtplib.SMTP_SSL('smtp.gmail.com',465), login via os.getenv(\"EMAIL_SENDER\u002FPASSWORD\"), attach PDF, dynamic subject. Secure creds in GitHub Secrets (EMAIL_SENDER, EMAIL_PASSWORD, EMAIL_RECEIVER).",[22,4210,4211],{},"Deploy via .github\u002Fworkflows\u002Fauto-report.yml:",[4040,4213,4217],{"className":4214,"code":4215,"language":4216,"meta":45,"style":45},"language-yaml shiki shiki-themes github-light github-dark","on:\n  schedule:\n    - cron: '0 1 * * 1'  # Mondays 1AM UTC\njobs:\n  # setup env, pip install, run main.py\n","yaml",[26,4218,4219,4229,4237,4256,4263],{"__ignoreMap":45},[3956,4220,4221,4225],{"class":4048,"line":4049},[3956,4222,4224],{"class":4223},"sj4cs","on",[3956,4226,4228],{"class":4227},"sVt8B",":\n",[3956,4230,4231,4235],{"class":4048,"line":46},[3956,4232,4234],{"class":4233},"s9eBZ","  schedule",[3956,4236,4228],{"class":4227},[3956,4238,4239,4242,4245,4248,4252],{"class":4048,"line":3915},[3956,4240,4241],{"class":4227},"    - ",[3956,4243,4244],{"class":4233},"cron",[3956,4246,4247],{"class":4227},": ",[3956,4249,4251],{"class":4250},"sZZnC","'0 1 * * 1'",[3956,4253,4255],{"class":4254},"sJ8bj","  # Mondays 1AM UTC\n",[3956,4257,4258,4261],{"class":4048,"line":3914},[3956,4259,4260],{"class":4233},"jobs",[3956,4262,4228],{"class":4227},[3956,4264,4265],{"class":4048,"line":4005},[3956,4266,4267],{"class":4254},"  # setup env, pip install, run main.py\n",[22,4269,4270],{},"Triggers workflow: installs deps, executes pipeline (advances run_count), generates\u002Fsends report. No local runs—wake to delivered emails. Full loop: cron → ETL → PDF → email → state update for next cutoff.",[22,4272,4273],{},"Trade-offs: Relies on GitHub free tier (2k min\u002Fmonth); Gmail app passwords needed; rule-insights basic (extend with ML if needed). Scales to live data sources by swapping CSVs for APIs\u002FDBs.",[4275,4276,4277],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .s9eBZ, html code.shiki .s9eBZ{--shiki-default:#22863A;--shiki-dark:#85E89D}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}",{"title":45,"searchDepth":46,"depth":46,"links":4279},[4280,4281,4282],{"id":4034,"depth":46,"text":4035},{"id":4143,"depth":46,"text":4144},{"id":4204,"depth":46,"text":4205},[128],{"content_references":4285,"triage":4294},[4286,4290],{"type":4001,"title":4287,"author":4288,"url":4289,"context":3912},"Brazilian Ecommerce Public Dataset by Olist","Olist","https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Folistbr\u002Fbrazilian-ecommerce",{"type":3996,"title":4291,"author":4292,"url":4293,"context":3999},"Weekly-Business-Report-Automation","jihanKamilah","https:\u002F\u002Fgithub.com\u002FjihanKamilah\u002FWeekly-Business-Report-Automation\u002F",{"relevance":4005,"novelty":3915,"quality":3914,"actionability":4005,"composite":4295,"reasoning":4296},4.35,"Category: AI Automation. The article provides a detailed guide on automating weekly reports using a Python ETL pipeline, which directly addresses the audience's need for practical automation solutions. It includes specific code examples and actionable steps, making it highly relevant and immediately applicable for those building AI-powered products.","\u002Fsummaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary","2026-04-21 13:31:02","2026-04-21 15:26:14",{"title":4024,"description":45},{"loc":4297},"90a024f8fc9fd261","Learning Data","https:\u002F\u002Fmedium.com\u002Flearning-data\u002Fi-was-tired-of-weekly-reports-so-i-automated-the-entire-thing-f63f88de59ce?source=rss----eec44e936bf1---4","summaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary",[68,69,70,4307],"data-visualization","Load\u002Fmerge e-commerce datasets, compute revenue\u002Fprofit\u002FAOV\u002Fgrowth metrics, generate PDF with matplotlib\u002FReportLab charts and rule-based insights, email via smtplib, schedule weekly via GitHub Actions cron.",[],"ui9FcZ6uGNS1FckDV1aa-U2QNtxJCoCeuihBdEY63jo",{"id":4312,"title":4313,"ai":4314,"body":4319,"categories":4347,"created_at":53,"date_modified":53,"description":45,"extension":54,"faq":53,"featured":55,"kicker_label":53,"meta":4348,"navigation":57,"path":4349,"published_at":4350,"question":53,"scraped_at":53,"seo":4351,"sitemap":4352,"source_id":4353,"source_name":4303,"source_type":64,"source_url":65,"stem":4354,"tags":4355,"thumbnail_url":53,"tldr":4356,"tweet":53,"unknown_tags":4357,"__hash__":4358},"summaries\u002Fsummaries\u002Fpractical-oop-python-data-quality-toolkit-summary.md","Practical OOP: Python Data Quality Toolkit",{"provider":7,"model":8,"input_tokens":4315,"output_tokens":4316,"processing_time_ms":4317,"cost_usd":4318},3380,809,8486,0.00061355,{"type":14,"value":4320,"toc":4342},[4321,4325,4328,4332,4335,4339],[17,4322,4324],{"id":4323},"from-toy-examples-to-real-world-oop","From Toy Examples to Real-World OOP",[22,4326,4327],{},"Generic OOP tutorials often use abstract classes like animals or shapes that don't solve actual problems. Instead, apply OOP to create a data quality toolkit that checks datasets for issues like missing values, duplicates, and schema mismatches—directly usable in data pipelines.",[17,4329,4331],{"id":4330},"core-oop-structure-for-data-validators","Core OOP Structure for Data Validators",[22,4333,4334],{},"Define abstract base classes for validators (e.g., BaseValidator with validate() and report() methods). Extend with concrete classes like MissingValueValidator or DuplicateValidator. Each handles specific checks: MissingValueValidator scans for NaNs and computes percentages; DuplicateValidator identifies and counts repeats. This inheritance ensures consistent interfaces while customizing logic per rule.",[17,4336,4338],{"id":4337},"benefits-and-usage","Benefits and Usage",[22,4340,4341],{},"Encapsulate checks into a QualityChecker class that composes multiple validators, runs them on DataFrames, and aggregates reports into JSON or HTML. Trade-offs: Adds abstraction overhead but improves modularity, testability, and extensibility for growing validation needs. Integrate via simple API: checker = QualityChecker(validators); results = checker.validate(df). Content is thin RSS teaser; full article details code on Medium.",{"title":45,"searchDepth":46,"depth":46,"links":4343},[4344,4345,4346],{"id":4323,"depth":46,"text":4324},{"id":4330,"depth":46,"text":4331},{"id":4337,"depth":46,"text":4338},[52],{},"\u002Fsummaries\u002Fpractical-oop-python-data-quality-toolkit-summary","2026-04-08 21:21:17",{"title":4313,"description":45},{"loc":4349},"3bc99baf3e1a274b","summaries\u002Fpractical-oop-python-data-quality-toolkit-summary",[68,70],"Use OOP to build a reusable data quality toolkit in Python that validates real datasets, ditching toy examples for production-ready code.",[],"jJTXnZGT0inxfzWez5pDC3MXsSZ1ffUVqikWuQEyX8o"]